class_docstr: Encoder class for transaction data in Python lists
  
  Parameters
  ------------
  None
  
  Attributes
  ------------
  columns_: list
    List of unique names in the `X` input list of lists
  
  Examples
  ------------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/
docstr: Fit a TransactionEncoder encoder and transform a dataset.
docstr_tok: ["Fit","a","TransactionEncoder","encoder","and","transform","a","dataset","."]
code: def fit_transform(self, X, sparse=False):
      """Fit a TransactionEncoder encoder and transform a dataset."""
      return self.fit(X).transform(X, sparse=sparse)
  
file_path: mlxtend/mlxtend/preprocessing/transactionencoder.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 5 for calling the TransactionEncoder obj's underlying member method code with example usage: obj.fit_transform(X, sparse) and returns: self